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MF-LOGP, a new method for determining a single component octanol–water partition coefficients ($$LogP$$$\mathrm{LogP}$) is presented which uses molecular formula as the only input. Octanol–water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make$$LogP$$$\mathrm{LogP}$predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average$$RMSE$$$\mathrm{RMSE}$= 0.77 ± 0.007,$$MAE$$$\mathrm{MAE}$= 0.52 ± 0.003, and$${R}^{2}$$$R^2$= 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ($$RMSE$$$\mathrm{RMSE}$= 0.42–1.54,$$MAE$$$\mathrm{MAE}$= 0.09–1.07, and$${R}^{2}$$$R^2$= 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.

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In this study, four geopolymer sorbents GP0, GP10, GP30 and GP50 were synthesized using volcanic ash (VA) and metakaolin (MK) blends as precursors with 0, 10, 30 and 50% MK content by mass, respectively. The materials were characterized by X-ray fuorescence (XRF), X-ray difraction (XRD), Raman spectroscopy, and Brunauer–Emmett–Teller (BET) surface area analyses, revealing successful geopolymerization of the precursors and increasing surface area with increasing MK content. The sorption performance of the VA, MK and VA-MK geopolymers was then evaluated for the removal of cationic methylene blue (MB) dye from aqueous media. Sorption capacity was independent of composition, providing fexibility in sorbent synthesis. Sorption rate, on the other hand, was 3–8 times greater for the VA-MK geopolymers than the precursor materials. The equilibrium adsorption data were suitably explained by the Freundlich model, denoting multilayer adsorption onto a heterogeneous adsorption surface with higher Freundlich afnity constant (KF) for geopolymers than VA. The adsorption kinetics obeyed the pseudo-second-order (PSO) kinetic law with an average of 98% removal efciency in 30 min. MB uptake was pH-dependent and driven by electrostatic chemisorption interactions. These results motivate further studies on the use of locally sourced geopolymers for water purifcation applications. 

Regression ensembles consisting of a collection of base regression models are often used to improve the estimation/prediction performance of a single regression model. It has been shown that the individual accuracy of the base models and the ensemble diversity are the two key factors affecting the performance of an ensemble. In this paper, we derive a theory for regression ensembles that illustrates the subtle trade-off between individual accuracy and ensemble diversity from the perspective of statistical correlations. Then, inspired by our derived theory, we further propose a novel loss function and a training algorithm for deep learning regression ensembles. We then demonstrate the advantage of our training approach over standard regression ensemble methods including random forest and gradient boosting regressors with both benchmark regression problems and chemical sensor problems involving analysis of Raman spectroscopy. Our key contribution is that our loss function and training algorithm is able to manage diversity explicitly in an ensemble, rather than merely allowing diversity to occur by happenstance. 

Mechanical decrystallization and water-promoted recrystallization of cellulose were studied to understand the effects of cellulose crystallinity on reaction engineering models of its acid-catalyzed hydrolysis. Microcrystalline cellulose was ball-milled for different periods of time, which decreased its crystallinity and increased the glucose yield obtained from acid hydrolysis treatment. Crystallinity increased after acid hydrolysis treatment, which has previously been explained in terms of rapid hydrolysis of amorphous cellulose, despite conflicting evidence of solvent promoted recrystallization. To elucidate the mechanism, decrystallized samples were subjected to various non-hydrolyzing treatments involving water exposure. Interestingly, all non-hydrolyzing hydrothermal treatments resulted in recovery of crystallinity, including a treatment consisting of heat-up and quenching that was selected as a way to estimate the crystallinity at the onset of hydrolysis. Therefore, the proposed mechanism involving rapid hydrolysis of amorphous cellulose must be incomplete, since the recrystallization rate of amorphous cellulose is greater than the hydrolysis rate. Several techniques (solid-state nuclear magnetic resonance, X-ray diffraction, and Raman spectroscopy) were used to establish that water contact promotes conversion of amorphous cellulose to a mixture of crystalline cellulose I and cellulose II. Crystallite size may also be reduced by the decrystallization-recrystallization treatment. Ethanolysis was used to confirm that the reactivity of the cellulose I/cellulose II mixture is distinct from that of truly amorphous cellulose. These results strongly point to a revised, more realistic model of hydrolysis of mechanically decrystallized cellulose, involving recrystallization and hydrolysis of the cellulose I/cellulose II mixture. 

Dodecane cracking and aromatization over ZSM‐5 was studied in the presence and absence of supercritical water (SCW). A group‐type model was used to determine five best‐fit rate constants to describe yields to aliphatics, aromatics, coke, and gases. SCW accelerated gas formation while suppressing coke formation. CO and CO₂were formed in the presence of SCW, but not in its absence; a new, low‐temperature coke gasification pathway was suggested to account for this observation. Similarly, a low‐temperature alkane reforming pathway was hypothesized to explain the increased relative rate constant for production of gases in the presence of SCW compared with its absence. Additional tests and analysis indicated that these effects could not be ascribed solely to zeolite degradation in the presence of SCW, implying that water directly influences the reaction mechanism. These results provide new insights into the role(s) of water during oil cracking under supercritical conditions.